Edge Detection

Edge detection, the identification of boundaries and significant intensity changes in images, aims to extract crucial structural information for various computer vision tasks. Current research emphasizes developing accurate, efficient, and robust edge detection methods, focusing on encoder-decoder architectures, transformers, and diffusion models, often incorporating multi-scale feature fusion and novel loss functions to address challenges like noisy labels and imbalanced datasets. These advancements improve performance across diverse applications, including medical image analysis, autonomous driving, and remote sensing, by providing more precise and reliable input for higher-level vision systems. The field is also exploring self-supervised learning techniques to reduce reliance on labor-intensive manual annotation.